Loading…

A priori data-driven multi-clustered reservoir generation algorithm for echo state network

Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the res...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2015-04, Vol.10 (4), p.e0120750-e0120750
Main Authors: Li, Xiumin, Zhong, Ling, Xue, Fangzheng, Zhang, Anguo
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3
cites cdi_FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3
container_end_page e0120750
container_issue 4
container_start_page e0120750
container_title PloS one
container_volume 10
creator Li, Xiumin
Zhong, Ling
Xue, Fangzheng
Zhang, Anguo
description Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.
doi_str_mv 10.1371/journal.pone.0120750
format article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1673120821</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A422026316</galeid><doaj_id>oai_doaj_org_article_0c758b45c0e14ae28b2aa6480e8ee4ea</doaj_id><sourcerecordid>A422026316</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3</originalsourceid><addsrcrecordid>eNqNk12L1DAUhoso7rr6D0QLguhFx3y0aXsjDIsfAwsLfl14E84kp52sbTMm6aj_3sxOd5nKXkguEpLnfZNzck6SPKVkQXlJ31zZ0Q3QLbZ2wAWhjJQFuZec0pqzTDDC7x-tT5JH3l8RUvBKiIfJCSuqsmC1OE2-L9OtM9aZVEOATDuzwyHtxy6YTHWjD-hQpw49up01Lm1xQAfB2CGFro26sOnTxroU1camPkDAdMDwy7ofj5MHDXQen0zzWfL1_bsv5x-zi8sPq_PlRaZEzULGBWEK6pyuK6ANgxwUXwteI6ko6qbKK9CF0FUNjDGquGqw0Mg1lFoz3iA_S54ffLed9XJKi5dUlDxmpWI0EqsDoS1cyRhvD-6PtGDk9YZ1rQQXjOpQElUW1TovFEGaA7JqzQBEXhGsEHOE6PV2um1c96gVDsFBNzOdnwxmI1u7kzmvCyZYNHg1GTj7c0QfZG-8wq6DAe14_e5c1PE3eURf_IPeHd1EtRADMENj471qbyqXOWOECU5FpBZ3UHFo7I2KNdSYuD8TvJ4JIhPwd2hh9F6uPn_6f_by25x9ecRuELqw8bYb9zXl52B-AJWz3jtsbpNMidy3wE025L4F5NQCUfbs-INuRTc1z_8CP2wCXw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1673120821</pqid></control><display><type>article</type><title>A priori data-driven multi-clustered reservoir generation algorithm for echo state network</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Li, Xiumin ; Zhong, Ling ; Xue, Fangzheng ; Zhang, Anguo</creator><contributor>Gao, Zhong-Ke</contributor><creatorcontrib>Li, Xiumin ; Zhong, Ling ; Xue, Fangzheng ; Zhang, Anguo ; Gao, Zhong-Ke</creatorcontrib><description>Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0120750</identifier><identifier>PMID: 25875296</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Cluster Analysis ; Clustering ; Complexity ; Computer Simulation ; Database Management Systems ; Databases, Factual ; Entropy (Information theory) ; Evaluation ; Forecasts and trends ; Models, Neurological ; Neural Networks (Computer) ; Reservoirs ; Reservoirs (Water) ; Topology ; Windows (intervals)</subject><ispartof>PloS one, 2015-04, Vol.10 (4), p.e0120750-e0120750</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Li et al 2015 Li et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3</citedby><cites>FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1673120821/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1673120821?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25875296$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gao, Zhong-Ke</contributor><creatorcontrib>Li, Xiumin</creatorcontrib><creatorcontrib>Zhong, Ling</creatorcontrib><creatorcontrib>Xue, Fangzheng</creatorcontrib><creatorcontrib>Zhang, Anguo</creatorcontrib><title>A priori data-driven multi-clustered reservoir generation algorithm for echo state network</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Complexity</subject><subject>Computer Simulation</subject><subject>Database Management Systems</subject><subject>Databases, Factual</subject><subject>Entropy (Information theory)</subject><subject>Evaluation</subject><subject>Forecasts and trends</subject><subject>Models, Neurological</subject><subject>Neural Networks (Computer)</subject><subject>Reservoirs</subject><subject>Reservoirs (Water)</subject><subject>Topology</subject><subject>Windows (intervals)</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNk12L1DAUhoso7rr6D0QLguhFx3y0aXsjDIsfAwsLfl14E84kp52sbTMm6aj_3sxOd5nKXkguEpLnfZNzck6SPKVkQXlJ31zZ0Q3QLbZ2wAWhjJQFuZec0pqzTDDC7x-tT5JH3l8RUvBKiIfJCSuqsmC1OE2-L9OtM9aZVEOATDuzwyHtxy6YTHWjD-hQpw49up01Lm1xQAfB2CGFro26sOnTxroU1camPkDAdMDwy7ofj5MHDXQen0zzWfL1_bsv5x-zi8sPq_PlRaZEzULGBWEK6pyuK6ANgxwUXwteI6ko6qbKK9CF0FUNjDGquGqw0Mg1lFoz3iA_S54ffLed9XJKi5dUlDxmpWI0EqsDoS1cyRhvD-6PtGDk9YZ1rQQXjOpQElUW1TovFEGaA7JqzQBEXhGsEHOE6PV2um1c96gVDsFBNzOdnwxmI1u7kzmvCyZYNHg1GTj7c0QfZG-8wq6DAe14_e5c1PE3eURf_IPeHd1EtRADMENj471qbyqXOWOECU5FpBZ3UHFo7I2KNdSYuD8TvJ4JIhPwd2hh9F6uPn_6f_by25x9ecRuELqw8bYb9zXl52B-AJWz3jtsbpNMidy3wE025L4F5NQCUfbs-INuRTc1z_8CP2wCXw</recordid><startdate>20150413</startdate><enddate>20150413</enddate><creator>Li, Xiumin</creator><creator>Zhong, Ling</creator><creator>Xue, Fangzheng</creator><creator>Zhang, Anguo</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20150413</creationdate><title>A priori data-driven multi-clustered reservoir generation algorithm for echo state network</title><author>Li, Xiumin ; Zhong, Ling ; Xue, Fangzheng ; Zhang, Anguo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Complexity</topic><topic>Computer Simulation</topic><topic>Database Management Systems</topic><topic>Databases, Factual</topic><topic>Entropy (Information theory)</topic><topic>Evaluation</topic><topic>Forecasts and trends</topic><topic>Models, Neurological</topic><topic>Neural Networks (Computer)</topic><topic>Reservoirs</topic><topic>Reservoirs (Water)</topic><topic>Topology</topic><topic>Windows (intervals)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xiumin</creatorcontrib><creatorcontrib>Zhong, Ling</creatorcontrib><creatorcontrib>Xue, Fangzheng</creatorcontrib><creatorcontrib>Zhang, Anguo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agriculture Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xiumin</au><au>Zhong, Ling</au><au>Xue, Fangzheng</au><au>Zhang, Anguo</au><au>Gao, Zhong-Ke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A priori data-driven multi-clustered reservoir generation algorithm for echo state network</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-04-13</date><risdate>2015</risdate><volume>10</volume><issue>4</issue><spage>e0120750</spage><epage>e0120750</epage><pages>e0120750-e0120750</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25875296</pmid><doi>10.1371/journal.pone.0120750</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2015-04, Vol.10 (4), p.e0120750-e0120750
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_1673120821
source Publicly Available Content Database; PubMed Central
subjects Algorithms
Analysis
Cluster Analysis
Clustering
Complexity
Computer Simulation
Database Management Systems
Databases, Factual
Entropy (Information theory)
Evaluation
Forecasts and trends
Models, Neurological
Neural Networks (Computer)
Reservoirs
Reservoirs (Water)
Topology
Windows (intervals)
title A priori data-driven multi-clustered reservoir generation algorithm for echo state network
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T02%3A11%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20priori%20data-driven%20multi-clustered%20reservoir%20generation%20algorithm%20for%20echo%20state%20network&rft.jtitle=PloS%20one&rft.au=Li,%20Xiumin&rft.date=2015-04-13&rft.volume=10&rft.issue=4&rft.spage=e0120750&rft.epage=e0120750&rft.pages=e0120750-e0120750&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0120750&rft_dat=%3Cgale_plos_%3EA422026316%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1673120821&rft_id=info:pmid/25875296&rft_galeid=A422026316&rfr_iscdi=true